Classification of early tuberculosis states to guide research for improved care and prevention: an international Delphi consensus exercise
Year: 2024
Type: article
Abstract: The current active-latent paradigm of tuberculosis largely neglects the documented spectrum of disease. Inconsistency with regard to definitions, terminology, and diagnostic criteria for different tuberculosis states has limited the progress in research and product development that are needed to achieve tuberculosis elimination. We aimed to develop a new framework of classification for tuberculosis that accommodates key disease states but is sufficiently simple to support pragmatic research and implementation. Through an international Delphi exercise that involved 71 participants representing a wide range of disciplines, sectors, income settings, and geographies, consensus was reached on a set of conceptual states, related terminology, and research gaps. The International Consensus for Early TB (ICE-TB) framework distinguishes disease from infection by the presence of macroscopic pathology and defines two subclinical and two clinical tuberculosis states on the basis of reported symptoms or signs of tuberculosis, further differentiated by likely infectiousness. The presence of viable Mycobacterium tuberculosis and an associated host response are prerequisites for all states of infection and disease. Our framework provides a clear direction for tuberculosis research, which will, in time, improve tuberculosis clinical care and elimination policies.
Source: The Lancet Respiratory Medicine
Authors: Anna K Coussens, Syed M A Zaidi, Brian W Allwood, Puneet K Dewan, Glenda Gray, Mikashmi Kohli, Tamara Kredo, Ben J Marais, Guy B Marks, Leo Martinez, Morten Ruhwald, Thomas J Scriba, James A Seddon, Phumeza Tisile, Digby F Warner, Robert J Wilkinson, Hanif Esmail, Rein M G J Houben, David Alland, Marcel A Behr, Busisiwe B Beko, Erlina Burhan, Gavin Churchyard, Frank Cobelens, Justin T Denholm, Ryan Dinkele, Jerrold J Ellner, Razia Fatima, Kate A Haigh, Mark Hatherill, Katherine C Horton, Emily A Kendall, Palwasha Y Khan, Peter MacPherson, Stephanus T Malherbe, Vidya Mave, Simon C Mendelsohn, Munyaradzi Musvosvi, Elisa Nemes, Adam Penn-Nicholson, Dharanidharan Ramamurthy, Molebogeng X Rangaka, Suvanand Sahu, Alvaro Schwalb, Divya K Shah, Dylan Sheerin, Donald Simon, Adrie J C Steyn, Nguyen Thu Anh, Gerhard Walzl, Charlotte L Weller, Caroline ML Williams, Emily B Wong, Robin Wood, Yingda L Xie, Siyan Yi
Institutions: University of Cape Town, The University of Melbourne, Walter and Eliza Hall Institute of Medical Research, Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, MRC Clinical Trials Unit at UCL, National University of Medical Sciences, University College London, Stellenbosch University, Gates Foundation, South African Medical Research Council, FIND, The University of Sydney, Boston University, South African Tuberculosis Vaccine Initiative, Imperial College London, The Francis Crick Institute, London School of Hygiene & Tropical Medicine
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $6,300
Funders: Bill and Melinda Gates Foundation, Wellcome Trust, Francis Crick Institute, Cancer Research UK, Imperial College London, University College London, Commonwealth Scholarship Commission, South African Medical Research Council, Universiteit Stellenbosch, University of Cape Town, National Institutes of Health, Medical Research Council, National Health and Medical Research Council, Faculty of Medicine and Health, University of Sydney, National Institute of Allergy and Infectious Diseases, H2020 European Research Council
Awards: MICA: Radio+ TB trial development: Determining the optimal regimen for bacteriologically negative, radiographically apparent TB diagnosed usingnewtest, G2540682662, G4758841269, G4777944076, G5291926605, G54722676, Translating and field-validating an epigenetic blood test to predict TB infection and disease risk, G6725874233, What is Tuberculosis? Challenging the Current Paradigm of Tuberculosis Natural History using Mathematical Modelling Techniques, Aurum Clinical Trials Unit
OpenAlex ID: W4393099031